Mar 162013
 

nowheretogoIn today’s Guardian newspaper geneticist Steve Jones has a short column replying to a 7 year old child who had asked “Will humans evolve into a new species?“. Jones is known in the UK as the media’s favourite geneticist and evolutionary biologist; he is a frequent guest on media shows and contributor in print media. Unfortunately, although very polished, and far from incompetent, he really isn’t very good with the details. He seems to be a self-confident man and often promotes his personal (not very mainstream) views at the expense of what evolutionary geneticists in general think. I don’t like this much, especially when the places he does it are looking for science information as currently understood rather any one person’s views.

Replying to the 7 year old today he first talked about how the speciation process is driven primarily by natural selection (I’m not going to address that in this post though many would be uncomfortable with that idea too). In the second part of the column he goes on to run out his view that evolution has stopped for humans. I’m actually not going to pick apart this silly idea, though many others have, but really just to encourage him to publish as soon as possible. I haven’t found any academic paper in which he puts forward this view, though he has been talking about it in the media for approximately 20 years. If this idea were true it would be important, very important, and very interesting. I would love to read that paper. He should gather his evidence and publish it as soon as possible in a peer reviewed open access scientific journal. Or else shut up.

Some other scientists’ views on Steve Jones’ ideas:

Human evolution stopping? Wrong, wrong, wrong
No Virginia, evolution isn’t ending
Evolution, why it still happens (in pictures)
Steven Jones is being silly
Not the end of evolution again!
Some comments on Steve Jones and human evolution

Jul 262011
 

I have a project going at the moment to examine changes in intron diversity, size and location in animal genomes. I am always a bit frustrated with the way introns are treated in many genome characterisation papers- “the genome contained Y introns with mean intron size Xbp” is usually all we get. This sort of summary stat can hide all manner of interesting trends. One measure that is often useful is intron density but unfortunately there doesn’t seem to be any standardised way to use this measure. Density is often measured as ‘introns per gene’, which is a reasonable shorthand, although since genes vary in length very considerable both within and between genomes it makes quantitative analysis very difficult indeed. I have seen ‘introns per 10kb’! This is OK, but what number to choose? What if each study chooses a different number? ‘Introns per nucleotide’ will standardise this better, and although the number will be very small, we seem to manage just fine with small mutation rate numbers and the like. But the more I think about it the less simple this seems.

Introns per bond

Something that is often overlooked when calculating the number of introns per nucleotide is that introns do not insert into nucleotides but rather the phosphodiester bonds between them. I would suggest therefore that the most accurate and effective way to specify density would be introns per bond. It seems reasonable that counting nucleotides is a convenient shorthand for this, but actually this shorthand leads to small but persistent errors. This is an unfortunate consequence of genome annotation restricting itself to nucleotides but genomic processes sometimes targeting bonds.

In the cartoon of a gene above CDS represents the protein coding region and UTR stands for the 5′ and 3′ untranslated regions. The dashes between nucleotides represent phosphodiester bonds joining the nucleotides. There are 6 nucleototides in the 5′-UTR, 9 nucleotides in the CDS and 5 nucleotides in the 3′-UTR. What would happen if we were to insert an intron in this sequence at the boundary of one of the gene regions? In which gene region would it be counted?

Coding regions almost always begin with a codon specifying a methionine residue- the start codon ATG. Nothing preceding this A nucleotide is counted as part of the CDS. Coding regions finish with a termination codon, TGA in the example above. This A nucleotide is the end of the CDS. By usual practice therefore any intron inserting into the bond between the T and the A at the 5′ end of the CDS would not be counted as part of the CDS, nor would any intron inserting into the bond between the A and the A at the 3′ end of the CDS. This is quite reasonable in many ways, but defining UTRs by reference to the CDS (after the last nucleotide, before the first nucleotide) means that the CDS has one less bond per nucleotide than do the UTRs! The 5′-UTR here has 6 nucleotides and 6 bonds, the 3′-UTR has 5 nucleotides and 5 bonds, but the CDS has 9 nucleotides and only 8 bonds where an inserting intron would be labelled as a ‘CDS intron’.

Does this matter?

Both yes and no. Counts using introns/nucleotide will be very similar to introns/bond. I am not claiming that work needs to be repeated or that substantial errors put into question previous work. But there are two issues here

  1. We should do it right. Understanding the actual insertion process requires us to use the right language. We should label introns as inserting between nucleotides to avoid confusion. You may not be confused, but try writing a script that counts introns when everything is labelled by nucleotide position.
  2. We can’t yet be sure what difference correct counts make in large data sets. The age of genomics is here. We can study hundreds of thousands of introns from lots of species and treat this mass of data statistically. The numbers of introns/nucleotide and introns/bond may look similar to our eyes, but trusting our savannah ape brains to make the right call is a risky strategy with big numbers.
The lab now uses these ‘per bond’ counts in our genomic intron scripts, which will be released when the first paper is out. I think it would be great if there was a biological standard for intron density, maybe we should even give it a unit- a Gilbert perhaps could equal one intron/10-3 bonds?
This post may be cited using the DOI: http://dx.doi.org/10.6084/m9.figshare.708404 

Sep 122008
 

I just read a post at Mario’s Entangled Bank called “Running an Academic Lab Google Style“. Some interesting ideas that I was aware of but had kind of forgotten. When I was a postdoc I had lots of strong views on how research should be done and although I haven’t really changed my mind it is so much more convenient to not think too much and just do research the way everyone else does.

The thing that got me thinking was the idea that at Google engineers get 20% of their time to work on something company-related that interests them. What would happen if we gave the same freedom to our postdocs and students?

The Google Way: Give Engineers Room (NY TImes)
Google’s “20 percent time” in action (Official Google Blog)
Life at Google

Googling shows that there are a lot of skeptics out there who don’t see how it works or believe that it can work in other environments. Joe Beda works at Google and nicely summarises (Google 20% Time) the reasons why it works for Google and may not in many other environments.

“The intrapersonal environment at Google is very energizing. When someone comes up with a new idea, the most common response is excitement and a brainstorming session. Politics and who owns what area rarely enter into it. I don’t think that I’ve seen anyone really raise their voice and get into a huge knockdown drag out fight since coming to Google.

Can 20% time work at other companies? I’m sure that there are going to be others that try. However, I think that it is important to realize that it is a result of an environment and philosophy to development more than a cause. I don’t think that it is something that can be imposed in an independent way”

I think research labs are one of the few places where this sort of environment would work well and be quite naturally developed.

Funding agencies would probably have a fit at the prospect, although I think the outcome of those grants would probably be more effective in solving the big questions posed in the grant proposal than where the staff followed the initial grant-plan religiously. I honestly don’t believe productivity would be lower, maybe the opposite. Wikipedia reports on Google saying

“In a talk at Stanford University, Marissa Mayer, Google’s Vice President of Search Products and User Experience, stated that her analysis showed that half of the new product launches originated from the 20% time.”

Although I don’t want to lie, I’m putting this on a public blog after all, not explicitly telling the funding agencies would prevent the nice grant-people from getting stressed.

Now I’m not suggesting that postdocs and students be given Friday off to go to the beach. Even Google uses the term “company-related” which I guess would translate into “something relevant to the questions and work of the lab”. It is important to nurture creativity and a feeling of involvement in shaping the research direction and strategy of a lab. Maybe researchers should be actively encouraged to spend 20% of their time

  1. thinking deeply and laterally about big questions in the area
  2. fixing small but annoying technical issues
  3. testing the feasibility of wild and weird ideas
  4. learning about and working on related topics and techniques that may one day feed back into their main project.

I get the impression, though it may be apocryphal, that this sort of thing used to happen much more in the past. Now there are detailed programs of work in grant proposals, few people are on “soft money” and side-projects can be seen as unprofessional. I remember having lots of conversations about side-projects as a post-doc and PhD student. Despite having very supportive supervisors I never really thought they would get it and would see it as a waste of time. I hid those side-projects, discussing with labmates but not the PI, until they were at a developed stage with clear outputs. Looking back those projects have generated my best and most highly-cited papers! The 4 papers listed at the bottom contain my 3 highest cited publications. Think what might have happened if I had discussed other ideas with the knowledgeable PIs of those labs.

PhD; 2/3 papers came from side-projects
Postdoc; 1/3 papers came from side-projects
My first small grant in my own lab; 1/2 papers came from side-projects

Of course my personal research is one big collection of side-projects, I can do whatever I like. But on grant-funded work maybe I haven’t encouraged people to just explore enough. That sounds like a resolution to me- all I need now are the grants! I intend to implement this 20% time with my next appointed student or postdoc, but it may be a while before I know how its working.

The other idea that is strong at Google is talking and mixing with a wide pool of other employees. There seems to be a culture where no ideas are considered stupid. The concepts of interaction and productivity in personal projects are not unlinked. This is something I’ve been really keen on at Hull- Journal clubs, informal lab meetings, communal coffee time, after work pub “meetings”. Most breakthrough science is done over coffee, I am convinced of it, but it is unlikely to be a legitimate consumables expense on grant proposals any time soon.


Some publications from side-projects

  1. Gómez, A., M. Serra, G. R. Carvalho, and D. H. Lunt (2002) EVOLUTION 56:1431-1444. PDF Speciation in ancient cryptic species complexes: Evidence from the molecular phylogeny of Brachionus plicatilis (Rotifera)
  2. Lunt, D. H., and B. C. Hyman (1997) NATURE 387:247-247. PDF Animal mitochondrial DNA recombination
  3. Lunt, D. H., D. X. Zhang, J. M. Szymura, and G. M. Hewitt (1996) INSECT MOLECULAR BIOLOGY 5:153-165. PDF The insect cytochrome oxidase I gene: Evolutionary patterns and conserved primers for phylogenetic studies
  4. Szymura, J. M., D. H. Lunt, and G. M. Hewitt (1996) INSECT MOLECULAR BIOLOGY 5:127-139.The sequence and structure of the meadow grasshopper (Chorthippus parallelus) mitochondrial srRNA, ND2, COI, COII ATPase8 and 9 tRNA genes


Picture; brainstorming at Google, link